---
title: "Accessing Green Space in Melbourne: Measuring Inequity and Household Mobility"
author: "F Sharifi, A Nyggard, W Stone, I Levin"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
    
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = F)
```



```{r, include=FALSE, warning=FALSE}
rm(list = ls())
library(htmltools)
library(flexdashboard)

# shype file
library(rgdal)
library(leaflet)

# Title Geographic Data Analysis and Modeling 
library(raster)
# Bindings for the 'Geospatial' Data Abstraction, 
library(rgdal)
# preferred for loading in function; maybe better?
require(ggplot2)
require(rgeos)
require(sf)
require(sp)
library(DT)
library(plotly)
library(tidyverse)
library(kableExtra)
library(dplyr)
```
	
### **1. Introduction:**

```{r}
library(imager)
knitr::include_graphics("Input/inequlity.jpg")
```

***


- Rapid urbanisation is in many countries accompanied by rising inequality Within cities, rising socio-spatial inequality manifests itself in
the ability of some residents to outcompete other residents for the locations that provide access to key economic, physical and social infrastructure. 


- Inequality also manifests in the concentration of poorer health and a series of concentrated social problems (e.g. anti-social behaviours). 

- While many of the processes that contribute to shaping trends in inequality typically are not determined at the city level, the manifestations of inequality are local and often spatially concentrated.


### **2. Research Questions:** {data-commentary-width=400}

```{r}

knitr::include_graphics("Input/Boston.jpg")

```

***
One way for cities to deal with the manifestations of inequality is through the provision of green space.

**- Research Questions:**

**1.** *How does access to different types of green space vary for low-income households in Melbourne, Australia?* 

**2.** *How does low-income household relocation within metropolitan Melbourne affect access to green space over time?*

**- Study Area: Melbourne **

Rapid population growth

Urban Development and densification

Property Values growth 

Global climate changes



### **3. Data and Method:** {data-commentary-width=400}


```{r, include=FALSE}
border<- readOGR(dsn = "Input/melb_border", layer= "melb_border")

green<- readOGR(dsn = "Input/SA2_and_Green_Access-R", layer= "sa2_and_green")

```

   
```{r, warning=FALSE}

 color_pal <- colorNumeric(palette = "Greens",domain = quantile(green$g_16_Al, seq(0,1,.2), na.rm = T))

leaflet() %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(data= green, 
                color = "#636363",
                opacity = 0,
                fillColor = ~color_pal(green$g_16_Al),
                fillOpacity = 1,
                label = paste(green$g_16_Al)) %>%
addPolylines(data = border,
                color = "black",
                opacity =1,
                weight= 1.5)%>%
           addLegend(pal = color_pal, values = green$g_16_Al,                         opacity = 0.7, 
                    title = NULL,
                    position = "topright")

```


*** 

 
**- Data:**

**Geographical Units:** On average SA2s are approximately 7.5 km$_2$ contain some 5,900 households (HH). (ABS)

**Socio-Economic Data:** The proportion of low-income households (HH) in SA2, (lowest 40%) (ABS).  

**Green space Data:** Unlike previous studies we utilize
a measure of green space that incorporates all alternative green space locations weighted by distance and congestion (users), rather than proximity or share of locality (DWELP).


**- Method:**


**1. Local Indicators of Spatial Association (LISA):** to determine the existence of bivariate statistically significant spatial clusters of low-income proportion and green space.


**2. Mann-Whitney U Test:** to discern the spatial distributional relationship between low-income proportion and green space index.
  


### **4. Main Results:** {data-commentary-width=585}

```{r}

load("Input/map_2.rdata")

map_2

```


*** 


**- Result:** The distribution of green space within the Melbourne metropolitan
area is skewed towards wealthier neighbourhoods.


```{r, include=FALSE}
mobility <- read.csv("Input/matrix_moves_low.csv", header = TRUE) 
```


```{r, fig.width=6, fig.align='center'}

knitr::include_graphics("Input/manwiteny.png")

# mobility %>%
#   kbl() %>%
#   kable_styling((bootstrap_options = c("striped", "hover", "condensed", "responsive"))) %>%
#   row_spec(1, color = "blue")

```


### **5. The Causes of Inequlaity: ** {data-commentary-width=585}

```{r}

load("Input/map_3.rdata")

map_3

```


***

**There is a few studies address the historical and contemporary social-political processes that have caused the inequality patterns:** That means dwellings close to green spaces often trade at a price premium. As a result, poorer access to green space for low-income households could also arise out of lower purchasing power. Hence, there is a gap in our knowledge on the role of population mobility and residential relocation in shaping urban spatial patterns over time.


-**Mobility Analysis:**  Population mobility is reinforcing and accentuating socio-economic
inequality in access to green space over time.


-**Role of Urban planing:** Urban planning has, to date, not succeeded in countering urban trends that shape inequality of green space access for different
socio-economic groups.



```{r, fig.width=5, fig.align='center'}
knitr::include_graphics("Input/mobility.png") 
```





### **6. Conclusion and Policy Implication** {data-commentary-width=450}

```{r}
knitr::include_graphics("Input/20min.png")

```

***

- This study sheds light on how policy-making can intervene in this process. The results have implications for municipal decision-making in service and resource allocations and distribution in Australian and global cities.

- In metropolitan Melbourne, we suggest prioritizing and implementing green
space provision plans for low-green areas in which relatively high proportions of lower socioeconomic households reside.

- Local governance planning need to monitor access to green space as part of routine analyses of urban equality.

- We suggest employing statistical and geographical tools for quantifying inequality, gauging the direction of in/equality trends and considering the results from an urban planning perspective. 

- We suggest further investigation of the potential causes of inequality, to help control them before negative outcomes become irreversible. 

- This method at least helps planners to recognize when and where there are needs for potential actions.